Enterprise Value (EV) to EBITDA

  • Enterprise Value (EV) = the total market value (MV) of the firm.

EV = MV of debt + MV preferred equity + MV common equity - Cash and investments

Cash and investments are netted out because these items reduce the net cost of purchasing the company.

  • Enterprise value is a commonly used valuation perspective in M&A and investment banking transaction analysis.
  • EBITDA = earnings before interest, taxes, depreciation and amortization

EBITDA = Net Income + Taxes + Interest Expense + Depreciation + Amortization

EBITDA is commonly used to approximate operational cash flows that are available to suppliers of debt and equity capital

  • EV/EBITDA ratio is done on a total and not per share basis as it reflects value for all suppliers of capital.

  • Positives of EV/EBITDA

  • Can be used when comparing firms with different degrees of financial leverage because the P/E ratio reflects value after interest has been paid.

  • The ratio will help make companies with high, but varying degrees of depreciation and amortization more comparable.

  • EBITDA is less likely to be negative than earnings.

  • Negatives of EV/EBITDA

  • EBITDA can overstate operational cash flows in instances where working capital is growing.

  • EBITDA will not reflect variances in revenue recognition practices across companies, and this impacts cash flow from operations.

  • EBITDA is not as suitable for total firm valuation as Free Cash Flow to the Firm.

  • Justifiable EV/EBITDA ratio: as FCFF rises, the multiple will increase; as WACC rises, the multiple will decrease.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.